Overview

Dataset statistics

Number of variables31
Number of observations40060
Missing cells464
Missing cells (%)< 0.1%
Duplicate rows2325
Duplicate rows (%)5.8%
Total size in memory9.5 MiB
Average record size in memory248.0 B

Variable types

Categorical19
Numeric12

Alerts

Dataset has 2325 (5.8%) duplicate rowsDuplicates
Country has a high cardinality: 125 distinct valuesHigh cardinality
Agent has a high cardinality: 186 distinct valuesHigh cardinality
Company has a high cardinality: 236 distinct valuesHigh cardinality
ReservationStatusDate has a high cardinality: 913 distinct valuesHigh cardinality
LeadTime is highly overall correlated with StaysInWeekNightsHigh correlation
ArrivalDateWeekNumber is highly overall correlated with ArrivalDateMonthHigh correlation
StaysInWeekendNights is highly overall correlated with StaysInWeekNightsHigh correlation
StaysInWeekNights is highly overall correlated with LeadTime and 1 other fieldsHigh correlation
IsCanceled is highly overall correlated with ReservationStatusHigh correlation
ArrivalDateMonth is highly overall correlated with ArrivalDateWeekNumberHigh correlation
MarketSegment is highly overall correlated with DistributionChannelHigh correlation
DistributionChannel is highly overall correlated with MarketSegmentHigh correlation
ReservedRoomType is highly overall correlated with AssignedRoomTypeHigh correlation
AssignedRoomType is highly overall correlated with ReservedRoomTypeHigh correlation
ReservationStatus is highly overall correlated with IsCanceledHigh correlation
Children is highly imbalanced (77.7%)Imbalance
Babies is highly imbalanced (93.3%)Imbalance
Meal is highly imbalanced (54.6%)Imbalance
Country is highly imbalanced (56.6%)Imbalance
IsRepeatedGuest is highly imbalanced (73.8%)Imbalance
DepositType is highly imbalanced (81.8%)Imbalance
Company is highly imbalanced (89.4%)Imbalance
RequiredCarParkingSpaces is highly imbalanced (74.9%)Imbalance
Country has 464 (1.2%) missing valuesMissing
Adults is highly skewed (γ1 = 31.61075868)Skewed
LeadTime has 3236 (8.1%) zerosZeros
StaysInWeekendNights has 14181 (35.4%) zerosZeros
StaysInWeekNights has 2682 (6.7%) zerosZeros
PreviousCancellations has 38965 (97.3%) zerosZeros
PreviousBookingsNotCanceled has 38028 (94.9%) zerosZeros
BookingChanges has 32252 (80.5%) zerosZeros
DaysInWaitingList has 39805 (99.4%) zerosZeros
ADR has 751 (1.9%) zerosZeros
TotalOfSpecialRequests has 22361 (55.8%) zerosZeros

Reproduction

Analysis started2023-11-24 17:45:29.492562
Analysis finished2023-11-24 17:45:42.476873
Duration12.98 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

IsCanceled
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
0
28938 
1
11122 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40060
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28938
72.2%
1 11122
 
27.8%

Length

2023-11-24T11:45:42.511058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:42.572276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28938
72.2%
1 11122
 
27.8%

Most occurring characters

ValueCountFrequency (%)
0 28938
72.2%
1 11122
 
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40060
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28938
72.2%
1 11122
 
27.8%

Most occurring scripts

ValueCountFrequency (%)
Common 40060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28938
72.2%
1 11122
 
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28938
72.2%
1 11122
 
27.8%

LeadTime
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct412
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.675686
Minimum0
Maximum737
Zeros3236
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:42.629310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median57
Q3155
95-th percentile287
Maximum737
Range737
Interquartile range (IQR)145

Descriptive statistics

Standard deviation97.285315
Coefficient of variation (CV)1.0497394
Kurtosis0.71548823
Mean92.675686
Median Absolute Deviation (MAD)54
Skewness1.1327159
Sum3712588
Variance9464.4326
MonotonicityNot monotonic
2023-11-24T11:45:42.693909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3236
 
8.1%
1 1595
 
4.0%
2 939
 
2.3%
3 794
 
2.0%
4 663
 
1.7%
5 600
 
1.5%
7 573
 
1.4%
6 538
 
1.3%
10 391
 
1.0%
8 388
 
1.0%
Other values (402) 30343
75.7%
ValueCountFrequency (%)
0 3236
8.1%
1 1595
4.0%
2 939
 
2.3%
3 794
 
2.0%
4 663
 
1.7%
5 600
 
1.5%
6 538
 
1.3%
7 573
 
1.4%
8 388
 
1.0%
9 368
 
0.9%
ValueCountFrequency (%)
737 1
 
< 0.1%
709 1
 
< 0.1%
542 23
0.1%
532 1
 
< 0.1%
471 6
 
< 0.1%
468 47
0.1%
462 20
< 0.1%
460 3
 
< 0.1%
454 1
 
< 0.1%
450 1
 
< 0.1%

ArrivalDateYear
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
2016
18567 
2017
13179 
2015
8314 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters160240
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2016 18567
46.3%
2017 13179
32.9%
2015 8314
20.8%

Length

2023-11-24T11:45:42.752639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:42.808594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 18567
46.3%
2017 13179
32.9%
2015 8314
20.8%

Most occurring characters

ValueCountFrequency (%)
2 40060
25.0%
0 40060
25.0%
1 40060
25.0%
6 18567
11.6%
7 13179
 
8.2%
5 8314
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 40060
25.0%
0 40060
25.0%
1 40060
25.0%
6 18567
11.6%
7 13179
 
8.2%
5 8314
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 160240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 40060
25.0%
0 40060
25.0%
1 40060
25.0%
6 18567
11.6%
7 13179
 
8.2%
5 8314
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 40060
25.0%
0 40060
25.0%
1 40060
25.0%
6 18567
11.6%
7 13179
 
8.2%
5 8314
 
5.2%

ArrivalDateMonth
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
August
4894 
July
4573 
April
3609 
May
3559 
October
3555 
Other values (7)
19870 

Length

Max length9
Median length7
Mean length5.9648028
Min length3

Characters and Unicode

Total characters238950
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August 4894
12.2%
July 4573
11.4%
April 3609
9.0%
May 3559
8.9%
October 3555
8.9%
March 3336
8.3%
September 3108
7.8%
February 3103
7.7%
June 3045
7.6%
December 2648
6.6%
Other values (2) 4630
11.6%

Length

2023-11-24T11:45:42.860171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 4894
12.2%
july 4573
11.4%
april 3609
9.0%
may 3559
8.9%
october 3555
8.9%
march 3336
8.3%
september 3108
7.8%
february 3103
7.7%
june 3045
7.6%
december 2648
6.6%
Other values (2) 4630
11.6%

Most occurring characters

ValueCountFrequency (%)
e 31845
13.3%
r 27092
 
11.3%
u 22702
 
9.5%
b 14851
 
6.2%
a 14384
 
6.0%
y 13428
 
5.6%
t 11557
 
4.8%
J 9811
 
4.1%
c 9539
 
4.0%
A 8503
 
3.6%
Other values (16) 75238
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 198890
83.2%
Uppercase Letter 40060
 
16.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31845
16.0%
r 27092
13.6%
u 22702
11.4%
b 14851
7.5%
a 14384
 
7.2%
y 13428
 
6.8%
t 11557
 
5.8%
c 9539
 
4.8%
m 8193
 
4.1%
l 8182
 
4.1%
Other values (8) 37117
18.7%
Uppercase Letter
ValueCountFrequency (%)
J 9811
24.5%
A 8503
21.2%
M 6895
17.2%
O 3555
 
8.9%
S 3108
 
7.8%
F 3103
 
7.7%
D 2648
 
6.6%
N 2437
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 238950
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31845
13.3%
r 27092
 
11.3%
u 22702
 
9.5%
b 14851
 
6.2%
a 14384
 
6.0%
y 13428
 
5.6%
t 11557
 
4.8%
J 9811
 
4.1%
c 9539
 
4.0%
A 8503
 
3.6%
Other values (16) 75238
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31845
13.3%
r 27092
 
11.3%
u 22702
 
9.5%
b 14851
 
6.2%
a 14384
 
6.0%
y 13428
 
5.6%
t 11557
 
4.8%
J 9811
 
4.1%
c 9539
 
4.0%
A 8503
 
3.6%
Other values (16) 75238
31.5%

ArrivalDateWeekNumber
Real number (ℝ)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.140864
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:42.919058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.005441
Coefficient of variation (CV)0.51602783
Kurtosis-1.0320734
Mean27.140864
Median Absolute Deviation (MAD)11
Skewness-0.010016716
Sum1087263
Variance196.15238
MonotonicityNot monotonic
2023-11-24T11:45:43.141035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 1197
 
3.0%
31 1101
 
2.7%
34 1090
 
2.7%
30 1076
 
2.7%
32 1054
 
2.6%
29 1037
 
2.6%
18 1012
 
2.5%
28 988
 
2.5%
35 951
 
2.4%
43 917
 
2.3%
Other values (43) 29637
74.0%
ValueCountFrequency (%)
1 343
 
0.9%
2 457
1.1%
3 553
1.4%
4 519
1.3%
5 501
1.3%
6 682
1.7%
7 904
2.3%
8 719
1.8%
9 795
2.0%
10 727
1.8%
ValueCountFrequency (%)
53 618
1.5%
52 592
1.5%
51 430
1.1%
50 449
1.1%
49 716
1.8%
48 493
1.2%
47 708
1.8%
46 514
1.3%
45 690
1.7%
44 726
1.8%

ArrivalDateDayOfMonth
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.821243
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:43.202720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.8837079
Coefficient of variation (CV)0.56150505
Kurtosis-1.1991534
Mean15.821243
Median Absolute Deviation (MAD)8
Skewness0.0063741858
Sum633799
Variance78.920266
MonotonicityNot monotonic
2023-11-24T11:45:43.257879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
12 1577
 
3.9%
30 1473
 
3.7%
5 1460
 
3.6%
16 1426
 
3.6%
9 1404
 
3.5%
26 1399
 
3.5%
17 1394
 
3.5%
2 1388
 
3.5%
18 1378
 
3.4%
24 1370
 
3.4%
Other values (21) 25791
64.4%
ValueCountFrequency (%)
1 1294
3.2%
2 1388
3.5%
3 1325
3.3%
4 1302
3.3%
5 1460
3.6%
6 1192
3.0%
7 1206
3.0%
8 1245
3.1%
9 1404
3.5%
10 1185
3.0%
ValueCountFrequency (%)
31 856
2.1%
30 1473
3.7%
29 1214
3.0%
28 1276
3.2%
27 1265
3.2%
26 1399
3.5%
25 1337
3.3%
24 1370
3.4%
23 1169
2.9%
22 1270
3.2%

StaysInWeekendNights
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1898153
Minimum0
Maximum19
Zeros14181
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:43.312010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1478122
Coefficient of variation (CV)0.96469782
Kurtosis7.5634483
Mean1.1898153
Median Absolute Deviation (MAD)1
Skewness1.3925394
Sum47664
Variance1.3174728
MonotonicityNot monotonic
2023-11-24T11:45:43.357919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 14181
35.4%
2 13975
34.9%
1 9192
22.9%
4 1558
 
3.9%
3 936
 
2.3%
6 113
 
0.3%
8 36
 
0.1%
5 35
 
0.1%
7 13
 
< 0.1%
12 5
 
< 0.1%
Other values (6) 16
 
< 0.1%
ValueCountFrequency (%)
0 14181
35.4%
1 9192
22.9%
2 13975
34.9%
3 936
 
2.3%
4 1558
 
3.9%
5 35
 
0.1%
6 113
 
0.3%
7 13
 
< 0.1%
8 36
 
0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 2
 
< 0.1%
13 2
 
< 0.1%
12 5
 
< 0.1%
10 5
 
< 0.1%
9 5
 
< 0.1%
8 36
 
0.1%
7 13
 
< 0.1%
6 113
0.3%

StaysInWeekNights
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1287319
Minimum0
Maximum50
Zeros2682
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:43.412732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile7
Maximum50
Range50
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4613294
Coefficient of variation (CV)0.78668594
Kurtosis15.627246
Mean3.1287319
Median Absolute Deviation (MAD)2
Skewness2.2526292
Sum125337
Variance6.0581425
MonotonicityNot monotonic
2023-11-24T11:45:43.471162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 9222
23.0%
5 7811
19.5%
2 7281
18.2%
3 5887
14.7%
4 3422
 
8.5%
0 2682
 
6.7%
6 1120
 
2.8%
10 891
 
2.2%
7 828
 
2.1%
8 496
 
1.2%
Other values (21) 420
 
1.0%
ValueCountFrequency (%)
0 2682
 
6.7%
1 9222
23.0%
2 7281
18.2%
3 5887
14.7%
4 3422
 
8.5%
5 7811
19.5%
6 1120
 
2.8%
7 828
 
2.1%
8 496
 
1.2%
9 158
 
0.4%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 1
 
< 0.1%
40 2
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 4
< 0.1%
26 1
 
< 0.1%
25 5
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%

Adults
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8671493
Minimum0
Maximum55
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:43.521796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69728549
Coefficient of variation (CV)0.37344925
Kurtosis1916.4281
Mean1.8671493
Median Absolute Deviation (MAD)0
Skewness31.610759
Sum74798
Variance0.48620706
MonotonicityNot monotonic
2023-11-24T11:45:43.563964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 31425
78.4%
1 7148
 
17.8%
3 1427
 
3.6%
4 31
 
0.1%
0 13
 
< 0.1%
26 5
 
< 0.1%
27 2
 
< 0.1%
20 2
 
< 0.1%
5 2
 
< 0.1%
40 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 13
 
< 0.1%
1 7148
 
17.8%
2 31425
78.4%
3 1427
 
3.6%
4 31
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
20 2
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
55 1
 
< 0.1%
50 1
 
< 0.1%
40 1
 
< 0.1%
27 2
 
< 0.1%
26 5
 
< 0.1%
20 2
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 31
0.1%

Children
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
0
36576 
1
 
1838
2
 
1628
3
 
17
10
 
1

Length

Max length2
Median length1
Mean length1.000025
Min length1

Characters and Unicode

Total characters40061
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36576
91.3%
1 1838
 
4.6%
2 1628
 
4.1%
3 17
 
< 0.1%
10 1
 
< 0.1%

Length

2023-11-24T11:45:43.613094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:43.672409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 36576
91.3%
1 1838
 
4.6%
2 1628
 
4.1%
3 17
 
< 0.1%
10 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36577
91.3%
1 1839
 
4.6%
2 1628
 
4.1%
3 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40061
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36577
91.3%
1 1839
 
4.6%
2 1628
 
4.1%
3 17
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 40061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36577
91.3%
1 1839
 
4.6%
2 1628
 
4.1%
3 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36577
91.3%
1 1839
 
4.6%
2 1628
 
4.1%
3 17
 
< 0.1%

Babies
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
0
39512 
1
 
539
2
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40060
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39512
98.6%
1 539
 
1.3%
2 9
 
< 0.1%

Length

2023-11-24T11:45:43.719717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:43.774056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 39512
98.6%
1 539
 
1.3%
2 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 39512
98.6%
1 539
 
1.3%
2 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40060
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39512
98.6%
1 539
 
1.3%
2 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 40060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39512
98.6%
1 539
 
1.3%
2 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39512
98.6%
1 539
 
1.3%
2 9
 
< 0.1%

Meal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
BB
30005 
HB
8046 
Undefined
 
1169
FB
 
754
SC
 
86

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters360540
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 30005
74.9%
HB 8046
 
20.1%
Undefined 1169
 
2.9%
FB 754
 
1.9%
SC 86
 
0.2%

Length

2023-11-24T11:45:43.818577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:43.877351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
bb 30005
74.9%
hb 8046
 
20.1%
undefined 1169
 
2.9%
fb 754
 
1.9%
sc 86
 
0.2%

Most occurring characters

ValueCountFrequency (%)
272237
75.5%
B 68810
 
19.1%
H 8046
 
2.2%
n 2338
 
0.6%
d 2338
 
0.6%
e 2338
 
0.6%
U 1169
 
0.3%
f 1169
 
0.3%
i 1169
 
0.3%
F 754
 
0.2%
Other values (2) 172
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 272237
75.5%
Uppercase Letter 78951
 
21.9%
Lowercase Letter 9352
 
2.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 68810
87.2%
H 8046
 
10.2%
U 1169
 
1.5%
F 754
 
1.0%
S 86
 
0.1%
C 86
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
n 2338
25.0%
d 2338
25.0%
e 2338
25.0%
f 1169
12.5%
i 1169
12.5%
Space Separator
ValueCountFrequency (%)
272237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272237
75.5%
Latin 88303
 
24.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 68810
77.9%
H 8046
 
9.1%
n 2338
 
2.6%
d 2338
 
2.6%
e 2338
 
2.6%
U 1169
 
1.3%
f 1169
 
1.3%
i 1169
 
1.3%
F 754
 
0.9%
S 86
 
0.1%
Common
ValueCountFrequency (%)
272237
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
272237
75.5%
B 68810
 
19.1%
H 8046
 
2.2%
n 2338
 
0.6%
d 2338
 
0.6%
e 2338
 
0.6%
U 1169
 
0.3%
f 1169
 
0.3%
i 1169
 
0.3%
F 754
 
0.2%
Other values (2) 172
 
< 0.1%

Country
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct125
Distinct (%)0.3%
Missing464
Missing (%)1.2%
Memory size313.1 KiB
PRT
17630 
GBR
6814 
ESP
3957 
IRL
2166 
FRA
 
1611
Other values (120)
7418 

Length

Max length3
Median length3
Mean length2.9820689
Min length2

Characters and Unicode

Total characters118078
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR

Common Values

ValueCountFrequency (%)
PRT 17630
44.0%
GBR 6814
 
17.0%
ESP 3957
 
9.9%
IRL 2166
 
5.4%
FRA 1611
 
4.0%
DEU 1203
 
3.0%
CN 710
 
1.8%
NLD 514
 
1.3%
USA 479
 
1.2%
ITA 459
 
1.1%
Other values (115) 4053
 
10.1%
(Missing) 464
 
1.2%

Length

2023-11-24T11:45:43.930211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prt 17630
44.5%
gbr 6814
 
17.2%
esp 3957
 
10.0%
irl 2166
 
5.5%
fra 1611
 
4.1%
deu 1203
 
3.0%
cn 710
 
1.8%
nld 514
 
1.3%
usa 479
 
1.2%
ita 459
 
1.2%
Other values (115) 4053
 
10.2%

Most occurring characters

ValueCountFrequency (%)
R 29444
24.9%
P 21978
18.6%
T 18427
15.6%
B 7744
 
6.6%
G 6958
 
5.9%
E 6451
 
5.5%
S 5138
 
4.4%
L 3710
 
3.1%
A 3558
 
3.0%
I 2889
 
2.4%
Other values (16) 11781
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 118078
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 29444
24.9%
P 21978
18.6%
T 18427
15.6%
B 7744
 
6.6%
G 6958
 
5.9%
E 6451
 
5.5%
S 5138
 
4.4%
L 3710
 
3.1%
A 3558
 
3.0%
I 2889
 
2.4%
Other values (16) 11781
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118078
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 29444
24.9%
P 21978
18.6%
T 18427
15.6%
B 7744
 
6.6%
G 6958
 
5.9%
E 6451
 
5.5%
S 5138
 
4.4%
L 3710
 
3.1%
A 3558
 
3.0%
I 2889
 
2.4%
Other values (16) 11781
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 29444
24.9%
P 21978
18.6%
T 18427
15.6%
B 7744
 
6.6%
G 6958
 
5.9%
E 6451
 
5.5%
S 5138
 
4.4%
L 3710
 
3.1%
A 3558
 
3.0%
I 2889
 
2.4%
Other values (16) 11781
10.0%

MarketSegment
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
Online TA
17729 
Offline TA/TO
7472 
Direct
6513 
Groups
5836 
Corporate
2309 

Length

Max length13
Median length9
Mean length8.841363
Min length6

Characters and Unicode

Total characters354185
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA 17729
44.3%
Offline TA/TO 7472
18.7%
Direct 6513
 
16.3%
Groups 5836
 
14.6%
Corporate 2309
 
5.8%
Complementary 201
 
0.5%

Length

2023-11-24T11:45:43.983012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:44.048666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
online 17729
27.2%
ta 17729
27.2%
offline 7472
11.4%
ta/to 7472
11.4%
direct 6513
 
10.0%
groups 5836
 
8.9%
corporate 2309
 
3.5%
complementary 201
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 43131
12.2%
e 34425
9.7%
O 32673
9.2%
T 32673
9.2%
i 31714
9.0%
l 25402
 
7.2%
25201
 
7.1%
A 25201
 
7.1%
r 17168
 
4.8%
f 14944
 
4.2%
Other values (13) 71653
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 216106
61.0%
Uppercase Letter 105406
29.8%
Space Separator 25201
 
7.1%
Other Punctuation 7472
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 43131
20.0%
e 34425
15.9%
i 31714
14.7%
l 25402
11.8%
r 17168
 
7.9%
f 14944
 
6.9%
o 10655
 
4.9%
t 9023
 
4.2%
p 8346
 
3.9%
c 6513
 
3.0%
Other values (5) 14785
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
O 32673
31.0%
T 32673
31.0%
A 25201
23.9%
D 6513
 
6.2%
G 5836
 
5.5%
C 2510
 
2.4%
Space Separator
ValueCountFrequency (%)
25201
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 7472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 321512
90.8%
Common 32673
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 43131
13.4%
e 34425
10.7%
O 32673
10.2%
T 32673
10.2%
i 31714
9.9%
l 25402
7.9%
A 25201
7.8%
r 17168
 
5.3%
f 14944
 
4.6%
o 10655
 
3.3%
Other values (11) 53526
16.6%
Common
ValueCountFrequency (%)
25201
77.1%
/ 7472
 
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 43131
12.2%
e 34425
9.7%
O 32673
9.2%
T 32673
9.2%
i 31714
9.0%
l 25402
 
7.2%
25201
 
7.1%
A 25201
 
7.1%
r 17168
 
4.8%
f 14944
 
4.2%
Other values (13) 71653
20.2%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
TA/TO
28925 
Direct
7865 
Corporate
3269 
Undefined
 
1

Length

Max length9
Median length5
Mean length5.5228407
Min length5

Characters and Unicode

Total characters221245
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 28925
72.2%
Direct 7865
 
19.6%
Corporate 3269
 
8.2%
Undefined 1
 
< 0.1%

Length

2023-11-24T11:45:44.111532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:44.177375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 28925
72.2%
direct 7865
 
19.6%
corporate 3269
 
8.2%
undefined 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 57850
26.1%
/ 28925
13.1%
O 28925
13.1%
A 28925
13.1%
r 14403
 
6.5%
e 11136
 
5.0%
t 11134
 
5.0%
i 7866
 
3.6%
c 7865
 
3.6%
D 7865
 
3.6%
Other values (8) 16351
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 126835
57.3%
Lowercase Letter 65485
29.6%
Other Punctuation 28925
 
13.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 14403
22.0%
e 11136
17.0%
t 11134
17.0%
i 7866
12.0%
c 7865
12.0%
o 6538
10.0%
p 3269
 
5.0%
a 3269
 
5.0%
n 2
 
< 0.1%
d 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 57850
45.6%
O 28925
22.8%
A 28925
22.8%
D 7865
 
6.2%
C 3269
 
2.6%
U 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 28925
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 192320
86.9%
Common 28925
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 57850
30.1%
O 28925
15.0%
A 28925
15.0%
r 14403
 
7.5%
e 11136
 
5.8%
t 11134
 
5.8%
i 7866
 
4.1%
c 7865
 
4.1%
D 7865
 
4.1%
o 6538
 
3.4%
Other values (7) 9813
 
5.1%
Common
ValueCountFrequency (%)
/ 28925
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 57850
26.1%
/ 28925
13.1%
O 28925
13.1%
A 28925
13.1%
r 14403
 
6.5%
e 11136
 
5.0%
t 11134
 
5.0%
i 7866
 
3.6%
c 7865
 
3.6%
D 7865
 
3.6%
Other values (8) 16351
 
7.4%

IsRepeatedGuest
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
0
38282 
1
 
1778

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40060
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38282
95.6%
1 1778
 
4.4%

Length

2023-11-24T11:45:44.227210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:44.279764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38282
95.6%
1 1778
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 38282
95.6%
1 1778
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40060
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38282
95.6%
1 1778
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 40060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38282
95.6%
1 1778
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38282
95.6%
1 1778
 
4.4%

PreviousCancellations
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10172242
Minimum0
Maximum26
Zeros38965
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:44.319413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3351153
Coefficient of variation (CV)13.125084
Kurtosis310.61435
Mean0.10172242
Median Absolute Deviation (MAD)0
Skewness17.417627
Sum4075
Variance1.7825328
MonotonicityNot monotonic
2023-11-24T11:45:44.366482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 38965
97.3%
1 896
 
2.2%
24 48
 
0.1%
2 44
 
0.1%
26 26
 
0.1%
25 25
 
0.1%
19 19
 
< 0.1%
3 14
 
< 0.1%
14 14
 
< 0.1%
4 6
 
< 0.1%
ValueCountFrequency (%)
0 38965
97.3%
1 896
 
2.2%
2 44
 
0.1%
3 14
 
< 0.1%
4 6
 
< 0.1%
5 3
 
< 0.1%
14 14
 
< 0.1%
19 19
 
< 0.1%
24 48
 
0.1%
25 25
 
0.1%
ValueCountFrequency (%)
26 26
 
0.1%
25 25
 
0.1%
24 48
 
0.1%
19 19
 
< 0.1%
14 14
 
< 0.1%
5 3
 
< 0.1%
4 6
 
< 0.1%
3 14
 
< 0.1%
2 44
 
0.1%
1 896
2.2%
Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14645532
Minimum0
Maximum30
Zeros38028
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:44.417831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0019545
Coefficient of variation (CV)6.8413668
Kurtosis244.37828
Mean0.14645532
Median Absolute Deviation (MAD)0
Skewness13.241518
Sum5867
Variance1.0039129
MonotonicityNot monotonic
2023-11-24T11:45:44.472718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 38028
94.9%
1 973
 
2.4%
2 388
 
1.0%
3 204
 
0.5%
4 127
 
0.3%
5 91
 
0.2%
6 56
 
0.1%
7 37
 
0.1%
8 33
 
0.1%
9 24
 
0.1%
Other values (21) 99
 
0.2%
ValueCountFrequency (%)
0 38028
94.9%
1 973
 
2.4%
2 388
 
1.0%
3 204
 
0.5%
4 127
 
0.3%
5 91
 
0.2%
6 56
 
0.1%
7 37
 
0.1%
8 33
 
0.1%
9 24
 
0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 2
< 0.1%
26 1
 
< 0.1%
25 3
< 0.1%
24 2
< 0.1%
23 2
< 0.1%
22 2
< 0.1%
21 2
< 0.1%

ReservedRoomType
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
A
23399 
D
7433 
E
4982 
G
 
1610
F
 
1106
Other values (5)
 
1530

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters640960
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 23399
58.4%
D 7433
 
18.6%
E 4982
 
12.4%
G 1610
 
4.0%
F 1106
 
2.8%
C 918
 
2.3%
H 601
 
1.5%
L 6
 
< 0.1%
B 3
 
< 0.1%
P 2
 
< 0.1%

Length

2023-11-24T11:45:44.527316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:44.592713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
a 23399
58.4%
d 7433
 
18.6%
e 4982
 
12.4%
g 1610
 
4.0%
f 1106
 
2.8%
c 918
 
2.3%
h 601
 
1.5%
l 6
 
< 0.1%
b 3
 
< 0.1%
p 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
600900
93.8%
A 23399
 
3.7%
D 7433
 
1.2%
E 4982
 
0.8%
G 1610
 
0.3%
F 1106
 
0.2%
C 918
 
0.1%
H 601
 
0.1%
L 6
 
< 0.1%
B 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 600900
93.8%
Uppercase Letter 40060
 
6.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 23399
58.4%
D 7433
 
18.6%
E 4982
 
12.4%
G 1610
 
4.0%
F 1106
 
2.8%
C 918
 
2.3%
H 601
 
1.5%
L 6
 
< 0.1%
B 3
 
< 0.1%
P 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
600900
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600900
93.8%
Latin 40060
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 23399
58.4%
D 7433
 
18.6%
E 4982
 
12.4%
G 1610
 
4.0%
F 1106
 
2.8%
C 918
 
2.3%
H 601
 
1.5%
L 6
 
< 0.1%
B 3
 
< 0.1%
P 2
 
< 0.1%
Common
ValueCountFrequency (%)
600900
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 640960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
600900
93.8%
A 23399
 
3.7%
D 7433
 
1.2%
E 4982
 
0.8%
G 1610
 
0.3%
F 1106
 
0.2%
C 918
 
0.1%
H 601
 
0.1%
L 6
 
< 0.1%
B 3
 
< 0.1%

AssignedRoomType
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
A
17046 
D
10339 
E
5638 
C
2214 
G
1853 
Other values (6)
2970 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters640960
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 17046
42.6%
D 10339
25.8%
E 5638
 
14.1%
C 2214
 
5.5%
G 1853
 
4.6%
F 1733
 
4.3%
H 712
 
1.8%
I 363
 
0.9%
B 159
 
0.4%
P 2
 
< 0.1%

Length

2023-11-24T11:45:44.654720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 17046
42.6%
d 10339
25.8%
e 5638
 
14.1%
c 2214
 
5.5%
g 1853
 
4.6%
f 1733
 
4.3%
h 712
 
1.8%
i 363
 
0.9%
b 159
 
0.4%
p 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
600900
93.8%
A 17046
 
2.7%
D 10339
 
1.6%
E 5638
 
0.9%
C 2214
 
0.3%
G 1853
 
0.3%
F 1733
 
0.3%
H 712
 
0.1%
I 363
 
0.1%
B 159
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 600900
93.8%
Uppercase Letter 40060
 
6.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 17046
42.6%
D 10339
25.8%
E 5638
 
14.1%
C 2214
 
5.5%
G 1853
 
4.6%
F 1733
 
4.3%
H 712
 
1.8%
I 363
 
0.9%
B 159
 
0.4%
P 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
600900
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600900
93.8%
Latin 40060
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17046
42.6%
D 10339
25.8%
E 5638
 
14.1%
C 2214
 
5.5%
G 1853
 
4.6%
F 1733
 
4.3%
H 712
 
1.8%
I 363
 
0.9%
B 159
 
0.4%
P 2
 
< 0.1%
Common
ValueCountFrequency (%)
600900
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 640960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
600900
93.8%
A 17046
 
2.7%
D 10339
 
1.6%
E 5638
 
0.9%
C 2214
 
0.3%
G 1853
 
0.3%
F 1733
 
0.3%
H 712
 
0.1%
I 363
 
0.1%
B 159
 
< 0.1%
Other values (2) 3
 
< 0.1%

BookingChanges
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28796805
Minimum0
Maximum17
Zeros32252
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:44.700438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72654759
Coefficient of variation (CV)2.5230146
Kurtosis37.894806
Mean0.28796805
Median Absolute Deviation (MAD)0
Skewness4.4244027
Sum11536
Variance0.5278714
MonotonicityNot monotonic
2023-11-24T11:45:44.749581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 32252
80.5%
1 5469
 
13.7%
2 1561
 
3.9%
3 460
 
1.1%
4 182
 
0.5%
5 72
 
0.2%
6 32
 
0.1%
7 12
 
< 0.1%
8 8
 
< 0.1%
9 4
 
< 0.1%
Other values (5) 8
 
< 0.1%
ValueCountFrequency (%)
0 32252
80.5%
1 5469
 
13.7%
2 1561
 
3.9%
3 460
 
1.1%
4 182
 
0.5%
5 72
 
0.2%
6 32
 
0.1%
7 12
 
< 0.1%
8 8
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
16 1
 
< 0.1%
13 2
 
< 0.1%
12 1
 
< 0.1%
10 3
 
< 0.1%
9 4
 
< 0.1%
8 8
 
< 0.1%
7 12
 
< 0.1%
6 32
0.1%
5 72
0.2%

DepositType
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
No Deposit
38199 
Non Refund
 
1719
Refundable
 
142

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters600900
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 38199
95.4%
Non Refund 1719
 
4.3%
Refundable 142
 
0.4%

Length

2023-11-24T11:45:44.803538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:44.859391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
no 38199
47.8%
deposit 38199
47.8%
non 1719
 
2.1%
refund 1719
 
2.1%
refundable 142
 
0.2%

Most occurring characters

ValueCountFrequency (%)
240218
40.0%
o 78117
 
13.0%
e 40202
 
6.7%
N 39918
 
6.6%
s 38199
 
6.4%
i 38199
 
6.4%
t 38199
 
6.4%
p 38199
 
6.4%
D 38199
 
6.4%
n 3580
 
0.6%
Other values (7) 7870
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 280704
46.7%
Space Separator 240218
40.0%
Uppercase Letter 79978
 
13.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 78117
27.8%
e 40202
14.3%
s 38199
13.6%
i 38199
13.6%
t 38199
13.6%
p 38199
13.6%
n 3580
 
1.3%
f 1861
 
0.7%
u 1861
 
0.7%
d 1861
 
0.7%
Other values (3) 426
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 39918
49.9%
D 38199
47.8%
R 1861
 
2.3%
Space Separator
ValueCountFrequency (%)
240218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360682
60.0%
Common 240218
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 78117
21.7%
e 40202
11.1%
N 39918
11.1%
s 38199
10.6%
i 38199
10.6%
t 38199
10.6%
p 38199
10.6%
D 38199
10.6%
n 3580
 
1.0%
R 1861
 
0.5%
Other values (6) 6009
 
1.7%
Common
ValueCountFrequency (%)
240218
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240218
40.0%
o 78117
 
13.0%
e 40202
 
6.7%
N 39918
 
6.6%
s 38199
 
6.4%
i 38199
 
6.4%
t 38199
 
6.4%
p 38199
 
6.4%
D 38199
 
6.4%
n 3580
 
0.6%
Other values (7) 7870
 
1.3%

Agent
Categorical

Distinct186
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
240
13905 
NULL
8209 
250
2869 
241
1721 
40
 
1002
Other values (181)
12354 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters440660
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.1%

Sample

1st row NULL
2nd row NULL
3rd row NULL
4th row 304
5th row 240

Common Values

ValueCountFrequency (%)
240 13905
34.7%
NULL 8209
20.5%
250 2869
 
7.2%
241 1721
 
4.3%
40 1002
 
2.5%
314 927
 
2.3%
242 779
 
1.9%
6 607
 
1.5%
96 537
 
1.3%
243 514
 
1.3%
Other values (176) 8990
22.4%

Length

2023-11-24T11:45:44.905475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
240 13905
34.7%
null 8209
20.5%
250 2869
 
7.2%
241 1721
 
4.3%
40 1002
 
2.5%
314 927
 
2.3%
242 779
 
1.9%
6 607
 
1.5%
96 537
 
1.3%
243 514
 
1.3%
Other values (176) 8990
22.4%

Most occurring characters

ValueCountFrequency (%)
318055
72.2%
2 23086
 
5.2%
4 21011
 
4.8%
0 18600
 
4.2%
L 16418
 
3.7%
N 8209
 
1.9%
U 8209
 
1.9%
1 7873
 
1.8%
5 5514
 
1.3%
3 4338
 
1.0%
Other values (4) 9347
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 318055
72.2%
Decimal Number 89769
 
20.4%
Uppercase Letter 32836
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 23086
25.7%
4 21011
23.4%
0 18600
20.7%
1 7873
 
8.8%
5 5514
 
6.1%
3 4338
 
4.8%
6 2926
 
3.3%
7 2303
 
2.6%
8 2206
 
2.5%
9 1912
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
L 16418
50.0%
N 8209
25.0%
U 8209
25.0%
Space Separator
ValueCountFrequency (%)
318055
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 407824
92.5%
Latin 32836
 
7.5%

Most frequent character per script

Common
ValueCountFrequency (%)
318055
78.0%
2 23086
 
5.7%
4 21011
 
5.2%
0 18600
 
4.6%
1 7873
 
1.9%
5 5514
 
1.4%
3 4338
 
1.1%
6 2926
 
0.7%
7 2303
 
0.6%
8 2206
 
0.5%
Latin
ValueCountFrequency (%)
L 16418
50.0%
N 8209
25.0%
U 8209
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 440660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
318055
72.2%
2 23086
 
5.2%
4 21011
 
4.8%
0 18600
 
4.2%
L 16418
 
3.7%
N 8209
 
1.9%
U 8209
 
1.9%
1 7873
 
1.8%
5 5514
 
1.3%
3 4338
 
1.0%
Other values (4) 9347
 
2.1%

Company
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct236
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
NULL
36952 
223
 
784
281
 
138
154
 
133
405
 
100
Other values (231)
 
1953

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters440660
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.2%

Sample

1st row NULL
2nd row NULL
3rd row NULL
4th row NULL
5th row NULL

Common Values

ValueCountFrequency (%)
NULL 36952
92.2%
223 784
 
2.0%
281 138
 
0.3%
154 133
 
0.3%
405 100
 
0.2%
94 87
 
0.2%
135 64
 
0.2%
331 58
 
0.1%
498 58
 
0.1%
47 56
 
0.1%
Other values (226) 1630
 
4.1%

Length

2023-11-24T11:45:44.952363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
null 36952
92.2%
223 784
 
2.0%
281 138
 
0.3%
154 133
 
0.3%
405 100
 
0.2%
94 87
 
0.2%
135 64
 
0.2%
331 58
 
0.1%
498 58
 
0.1%
47 56
 
0.1%
Other values (226) 1630
 
4.1%

Most occurring characters

ValueCountFrequency (%)
284028
64.5%
L 73904
 
16.8%
N 36952
 
8.4%
U 36952
 
8.4%
2 2353
 
0.5%
3 1673
 
0.4%
1 1019
 
0.2%
4 897
 
0.2%
5 626
 
0.1%
8 536
 
0.1%
Other values (4) 1720
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 284028
64.5%
Uppercase Letter 147808
33.5%
Decimal Number 8824
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2353
26.7%
3 1673
19.0%
1 1019
11.5%
4 897
 
10.2%
5 626
 
7.1%
8 536
 
6.1%
0 536
 
6.1%
9 494
 
5.6%
7 428
 
4.9%
6 262
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
L 73904
50.0%
N 36952
25.0%
U 36952
25.0%
Space Separator
ValueCountFrequency (%)
284028
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 292852
66.5%
Latin 147808
33.5%

Most frequent character per script

Common
ValueCountFrequency (%)
284028
97.0%
2 2353
 
0.8%
3 1673
 
0.6%
1 1019
 
0.3%
4 897
 
0.3%
5 626
 
0.2%
8 536
 
0.2%
0 536
 
0.2%
9 494
 
0.2%
7 428
 
0.1%
Latin
ValueCountFrequency (%)
L 73904
50.0%
N 36952
25.0%
U 36952
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 440660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
284028
64.5%
L 73904
 
16.8%
N 36952
 
8.4%
U 36952
 
8.4%
2 2353
 
0.5%
3 1673
 
0.4%
1 1019
 
0.2%
4 897
 
0.2%
5 626
 
0.1%
8 536
 
0.1%
Other values (4) 1720
 
0.4%

DaysInWaitingList
Real number (ℝ)

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52775836
Minimum0
Maximum185
Zeros39805
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:45.007156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum185
Range185
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.4285804
Coefficient of variation (CV)14.075723
Kurtosis251.48458
Mean0.52775836
Median Absolute Deviation (MAD)0
Skewness15.436884
Sum21142
Variance55.183807
MonotonicityNot monotonic
2023-11-24T11:45:45.066809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 39805
99.4%
122 55
 
0.1%
65 27
 
0.1%
47 23
 
0.1%
75 20
 
< 0.1%
101 18
 
< 0.1%
125 16
 
< 0.1%
150 11
 
< 0.1%
14 6
 
< 0.1%
60 5
 
< 0.1%
Other values (34) 74
 
0.2%
ValueCountFrequency (%)
0 39805
99.4%
1 5
 
< 0.1%
2 2
 
< 0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
6 4
 
< 0.1%
8 3
 
< 0.1%
11 1
 
< 0.1%
13 2
 
< 0.1%
14 6
 
< 0.1%
ValueCountFrequency (%)
185 2
 
< 0.1%
154 2
 
< 0.1%
150 11
 
< 0.1%
142 1
 
< 0.1%
125 16
 
< 0.1%
122 55
0.1%
121 2
 
< 0.1%
116 1
 
< 0.1%
113 5
 
< 0.1%
109 1
 
< 0.1%

CustomerType
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
Transient
30209 
Transient-Party
7791 
Contract
 
1776
Group
 
284

Length

Max length15
Median length9
Mean length10.094209
Min length5

Characters and Unicode

Total characters404374
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 30209
75.4%
Transient-Party 7791
 
19.4%
Contract 1776
 
4.4%
Group 284
 
0.7%

Length

2023-11-24T11:45:45.124402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:45.182492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
transient 30209
75.4%
transient-party 7791
 
19.4%
contract 1776
 
4.4%
group 284
 
0.7%

Most occurring characters

ValueCountFrequency (%)
n 77776
19.2%
t 49343
12.2%
r 47851
11.8%
a 47567
11.8%
T 38000
9.4%
s 38000
9.4%
i 38000
9.4%
e 38000
9.4%
y 7791
 
1.9%
- 7791
 
1.9%
Other values (7) 14255
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 348732
86.2%
Uppercase Letter 47851
 
11.8%
Dash Punctuation 7791
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 77776
22.3%
t 49343
14.1%
r 47851
13.7%
a 47567
13.6%
s 38000
10.9%
i 38000
10.9%
e 38000
10.9%
y 7791
 
2.2%
o 2060
 
0.6%
c 1776
 
0.5%
Other values (2) 568
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
T 38000
79.4%
P 7791
 
16.3%
C 1776
 
3.7%
G 284
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 7791
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 396583
98.1%
Common 7791
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 77776
19.6%
t 49343
12.4%
r 47851
12.1%
a 47567
12.0%
T 38000
9.6%
s 38000
9.6%
i 38000
9.6%
e 38000
9.6%
y 7791
 
2.0%
P 7791
 
2.0%
Other values (6) 6464
 
1.6%
Common
ValueCountFrequency (%)
- 7791
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 404374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 77776
19.2%
t 49343
12.2%
r 47851
11.8%
a 47567
11.8%
T 38000
9.4%
s 38000
9.4%
i 38000
9.4%
e 38000
9.4%
y 7791
 
1.9%
- 7791
 
1.9%
Other values (7) 14255
 
3.5%

ADR
Real number (ℝ)

Distinct5880
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.95293
Minimum-6.38
Maximum508
Zeros751
Zeros (%)1.9%
Negative1
Negative (%)< 0.1%
Memory size313.1 KiB
2023-11-24T11:45:45.241105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile30
Q150
median75
Q3125
95-th percentile221.0035
Maximum508
Range514.38
Interquartile range (IQR)75

Descriptive statistics

Standard deviation61.442418
Coefficient of variation (CV)0.64708291
Kurtosis1.3400805
Mean94.95293
Median Absolute Deviation (MAD)30
Skewness1.2373892
Sum3803814.4
Variance3775.1707
MonotonicityNot monotonic
2023-11-24T11:45:45.307982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 1024
 
2.6%
0 751
 
1.9%
68 489
 
1.2%
60 475
 
1.2%
80 463
 
1.2%
85 446
 
1.1%
35 417
 
1.0%
55 414
 
1.0%
40 396
 
1.0%
66 390
 
1.0%
Other values (5870) 34795
86.9%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 751
1.9%
0.26 1
 
< 0.1%
1.56 2
 
< 0.1%
1.8 1
 
< 0.1%
2 5
 
< 0.1%
2.4 1
 
< 0.1%
3 1
 
< 0.1%
4 21
 
0.1%
4.5 2
 
< 0.1%
ValueCountFrequency (%)
508 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%
388 2
< 0.1%
387 1
< 0.1%
384 1
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
0
34570 
1
5462 
2
 
25
8
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40060
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34570
86.3%
1 5462
 
13.6%
2 25
 
0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Length

2023-11-24T11:45:45.363061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:45.420120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 34570
86.3%
1 5462
 
13.6%
2 25
 
0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 34570
86.3%
1 5462
 
13.6%
2 25
 
0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40060
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34570
86.3%
1 5462
 
13.6%
2 25
 
0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 40060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34570
86.3%
1 5462
 
13.6%
2 25
 
0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34570
86.3%
1 5462
 
13.6%
2 25
 
0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

TotalOfSpecialRequests
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61977034
Minimum0
Maximum5
Zeros22361
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size313.1 KiB
2023-11-24T11:45:45.464904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8139296
Coefficient of variation (CV)1.3132761
Kurtosis1.2074689
Mean0.61977034
Median Absolute Deviation (MAD)0
Skewness1.2477349
Sum24828
Variance0.66248139
MonotonicityNot monotonic
2023-11-24T11:45:45.510775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 22361
55.8%
1 11806
29.5%
2 4827
 
12.0%
3 910
 
2.3%
4 142
 
0.4%
5 14
 
< 0.1%
ValueCountFrequency (%)
0 22361
55.8%
1 11806
29.5%
2 4827
 
12.0%
3 910
 
2.3%
4 142
 
0.4%
5 14
 
< 0.1%
ValueCountFrequency (%)
5 14
 
< 0.1%
4 142
 
0.4%
3 910
 
2.3%
2 4827
 
12.0%
1 11806
29.5%
0 22361
55.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
Check-Out
28938 
Canceled
10831 
No-Show
 
291

Length

Max length9
Median length9
Mean length8.7151023
Min length7

Characters and Unicode

Total characters349127
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 28938
72.2%
Canceled 10831
 
27.0%
No-Show 291
 
0.7%

Length

2023-11-24T11:45:45.563609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:45:45.626412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
check-out 28938
72.2%
canceled 10831
 
27.0%
no-show 291
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 50600
14.5%
C 39769
11.4%
c 39769
11.4%
h 29229
8.4%
- 29229
8.4%
u 28938
8.3%
t 28938
8.3%
O 28938
8.3%
k 28938
8.3%
a 10831
 
3.1%
Other values (7) 33948
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 250609
71.8%
Uppercase Letter 69289
 
19.8%
Dash Punctuation 29229
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 50600
20.2%
c 39769
15.9%
h 29229
11.7%
u 28938
11.5%
t 28938
11.5%
k 28938
11.5%
a 10831
 
4.3%
n 10831
 
4.3%
l 10831
 
4.3%
d 10831
 
4.3%
Other values (2) 873
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
C 39769
57.4%
O 28938
41.8%
N 291
 
0.4%
S 291
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 29229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 319898
91.6%
Common 29229
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 50600
15.8%
C 39769
12.4%
c 39769
12.4%
h 29229
9.1%
u 28938
9.0%
t 28938
9.0%
O 28938
9.0%
k 28938
9.0%
a 10831
 
3.4%
n 10831
 
3.4%
Other values (6) 23117
7.2%
Common
ValueCountFrequency (%)
- 29229
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 349127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 50600
14.5%
C 39769
11.4%
c 39769
11.4%
h 29229
8.4%
- 29229
8.4%
u 28938
8.3%
t 28938
8.3%
O 28938
8.3%
k 28938
8.3%
a 10831
 
3.1%
Other values (7) 33948
9.7%
Distinct913
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size313.1 KiB
2017-01-19
 
147
2016-02-09
 
145
2016-02-29
 
129
2017-02-28
 
124
2015-11-15
 
124
Other values (908)
39391 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters400600
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.1%

Sample

1st row2015-07-01
2nd row2015-07-01
3rd row2015-07-02
4th row2015-07-02
5th row2015-07-03

Common Values

ValueCountFrequency (%)
2017-01-19 147
 
0.4%
2016-02-09 145
 
0.4%
2016-02-29 129
 
0.3%
2017-02-28 124
 
0.3%
2015-11-15 124
 
0.3%
2016-10-06 121
 
0.3%
2015-12-08 115
 
0.3%
2017-03-06 114
 
0.3%
2017-05-25 114
 
0.3%
2016-04-04 110
 
0.3%
Other values (903) 38817
96.9%

Length

2023-11-24T11:45:45.673072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-01-19 147
 
0.4%
2016-02-09 145
 
0.4%
2016-02-29 129
 
0.3%
2017-02-28 124
 
0.3%
2015-11-15 124
 
0.3%
2016-10-06 121
 
0.3%
2015-12-08 115
 
0.3%
2017-03-06 114
 
0.3%
2017-05-25 114
 
0.3%
2016-04-04 110
 
0.3%
Other values (903) 38817
96.9%

Most occurring characters

ValueCountFrequency (%)
0 90317
22.5%
- 80120
20.0%
1 73097
18.2%
2 62895
15.7%
6 26071
 
6.5%
7 20218
 
5.0%
5 16092
 
4.0%
3 9521
 
2.4%
8 7966
 
2.0%
4 7211
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320480
80.0%
Dash Punctuation 80120
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90317
28.2%
1 73097
22.8%
2 62895
19.6%
6 26071
 
8.1%
7 20218
 
6.3%
5 16092
 
5.0%
3 9521
 
3.0%
8 7966
 
2.5%
4 7211
 
2.3%
9 7092
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 80120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 90317
22.5%
- 80120
20.0%
1 73097
18.2%
2 62895
15.7%
6 26071
 
6.5%
7 20218
 
5.0%
5 16092
 
4.0%
3 9521
 
2.4%
8 7966
 
2.0%
4 7211
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 90317
22.5%
- 80120
20.0%
1 73097
18.2%
2 62895
15.7%
6 26071
 
6.5%
7 20218
 
5.0%
5 16092
 
4.0%
3 9521
 
2.4%
8 7966
 
2.0%
4 7211
 
1.8%

Interactions

2023-11-24T11:45:41.188582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.073659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.871203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.654426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.354574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.087919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.890358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.570091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.254474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.958010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.803641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.512787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.247398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.172436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.932410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.713995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.416765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.146517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.950006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.629255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.314819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.021038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.864947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.569511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.302675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.262707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.986017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.771828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.476137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.202476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.005134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.684464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.372350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.079167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.921766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.624677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.361551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.336597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.043225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.829354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.538966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.259622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.063034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.742257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.431171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.139991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.981971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.681996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.421871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.399541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.104119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.890066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.601980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.320969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.122606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.802192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.491999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.202068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.043708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.741110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.478514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.457524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.160292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.946783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.661562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.375682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.177241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.857217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.549978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.260227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.101369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.796279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.533276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.514313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.214328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.002448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.722900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.430047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.231668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.911846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.605755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.317884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.158337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.849517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.589584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.571654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.270578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.060342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.784527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.484971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.286495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.966225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.663521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.375552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.215433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.904476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.647691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.632377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.328536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.118229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.846017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.545108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.343294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.024276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.721541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.563304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.275237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.961178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.708417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.695438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.482949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.180371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.907786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.608057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.401715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.084756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.784464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.624961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.337622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.022044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.767414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.756206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.542181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.240360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.970611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.778476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.460923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.143403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.845361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.687728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.397861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.079384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.823270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:33.813190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:34.598257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:35.297861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.028624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:36.834867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:37.515152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.198243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:38.902019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:39.745413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:40.454162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-24T11:45:41.133792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-11-24T11:45:45.737574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
LeadTimeArrivalDateWeekNumberArrivalDateDayOfMonthStaysInWeekendNightsStaysInWeekNightsAdultsPreviousCancellationsPreviousBookingsNotCanceledBookingChangesDaysInWaitingListADRTotalOfSpecialRequestsIsCanceledArrivalDateYearArrivalDateMonthChildrenBabiesMealMarketSegmentDistributionChannelIsRepeatedGuestReservedRoomTypeAssignedRoomTypeDepositTypeCustomerTypeRequiredCarParkingSpacesReservationStatus
LeadTime1.0000.128-0.0120.4210.5260.2350.114-0.2150.0760.0750.1570.0230.2350.1040.1700.0200.0000.1170.1980.1380.1410.0460.0510.1780.1470.0760.172
ArrivalDateWeekNumber0.1281.0000.0720.0650.0710.0680.066-0.0730.0140.0490.1390.0410.1080.4210.7990.0840.0330.0990.1540.1290.1250.0650.0510.1300.1460.0110.083
ArrivalDateDayOfMonth-0.0120.0721.000-0.028-0.0310.009-0.0300.005-0.0020.0300.0390.0120.0340.0370.0620.0130.0050.0500.0440.0530.0150.0140.0130.0960.0460.0100.026
StaysInWeekendNights0.4210.065-0.0281.0000.5740.183-0.015-0.1470.047-0.0010.1660.0680.0960.0400.0950.0320.0200.0940.1430.1260.1250.0630.0300.0350.1650.0500.070
StaysInWeekNights0.5260.071-0.0310.5741.0000.199-0.010-0.1590.0860.0470.2070.0690.0350.0300.0630.0000.0000.0720.0850.0440.0420.0380.0260.0310.1310.0400.025
Adults0.2350.0680.0090.1830.1991.000-0.030-0.272-0.027-0.0080.3950.1550.0280.0230.0190.0000.0000.0000.0120.0110.0000.0000.0000.0000.1270.0000.019
PreviousCancellations0.1140.066-0.030-0.015-0.010-0.0301.0000.113-0.047-0.012-0.077-0.0640.0950.0790.0680.0000.0000.0890.0550.0440.0660.0200.0310.1570.0070.0070.068
PreviousBookingsNotCanceled-0.215-0.0730.005-0.147-0.159-0.2720.1131.0000.007-0.009-0.149-0.0050.0580.0390.0220.0030.0000.0240.1190.1330.3090.0170.0080.0080.0370.0170.040
BookingChanges0.0760.014-0.0020.0470.086-0.027-0.0470.0071.0000.0210.0240.0130.0730.0300.0170.0310.0480.0830.0450.0570.0000.0130.0480.0390.0680.0260.053
DaysInWaitingList0.0750.0490.030-0.0010.047-0.008-0.012-0.0090.0211.000-0.006-0.0580.0350.0590.0620.0000.0000.1030.0720.0480.0000.0150.0170.1640.0830.0400.022
ADR0.1570.1390.0390.1660.2070.395-0.077-0.1490.024-0.0061.0000.1940.1140.1450.2870.1880.0420.1360.2160.1650.1640.1680.1350.1020.1300.0520.085
TotalOfSpecialRequests0.0230.0410.0120.0680.0690.155-0.064-0.0050.013-0.0580.1941.0000.1150.0760.0830.0420.1160.0730.2090.1420.0670.0710.0550.1360.1050.0400.081
IsCanceled0.2350.1080.0340.0960.0350.0280.0950.0580.0730.0350.1140.1151.0000.0470.1150.0870.0230.1100.2530.1360.1030.0810.1830.3230.1410.2471.000
ArrivalDateYear0.1040.4210.0370.0400.0300.0230.0790.0390.0300.0590.1450.0760.0471.0000.4280.0470.0100.0700.0960.0370.0750.0690.0380.0630.0530.0150.034
ArrivalDateMonth0.1700.7990.0620.0950.0630.0190.0680.0220.0170.0620.2870.0830.1150.4281.0000.0900.0360.1150.1650.1330.1220.0670.0480.1390.1530.0170.087
Children0.0200.0840.0130.0320.0000.0000.0000.0030.0310.0000.1880.0420.0870.0470.0901.0000.0480.0310.1010.0550.0460.4200.3570.0450.0660.0290.074
Babies0.0000.0330.0050.0200.0000.0000.0000.0000.0480.0000.0420.1160.0230.0100.0360.0481.0000.0200.0440.0290.0200.0590.0650.0170.0180.0250.015
Meal0.1170.0990.0500.0940.0720.0000.0890.0240.0830.1030.1360.0730.1100.0700.1150.0310.0201.0000.2070.0760.0740.1000.2340.1840.1450.0380.081
MarketSegment0.1980.1540.0440.1430.0850.0120.0550.1190.0450.0720.2160.2090.2530.0960.1650.1010.0440.2071.0000.6860.2730.1790.1500.3260.4060.0990.183
DistributionChannel0.1380.1290.0530.1260.0440.0110.0440.1330.0570.0480.1650.1420.1360.0370.1330.0550.0290.0760.6861.0000.2100.1520.1060.0630.1160.0760.099
IsRepeatedGuest0.1410.1250.0150.1250.0420.0000.0660.3090.0000.0000.1640.0670.1030.0750.1220.0460.0200.0740.2730.2101.0000.0750.0730.0440.1550.0600.104
ReservedRoomType0.0460.0650.0140.0630.0380.0000.0200.0170.0130.0150.1680.0710.0810.0690.0670.4200.0590.1000.1790.1520.0751.0000.7380.0910.1220.0590.058
AssignedRoomType0.0510.0510.0130.0300.0260.0000.0310.0080.0480.0170.1350.0550.1830.0380.0480.3570.0650.2340.1500.1060.0730.7381.0000.1270.1010.0730.131
DepositType0.1780.1300.0960.0350.0310.0000.1570.0080.0390.1640.1020.1360.3230.0630.1390.0450.0170.1840.3260.0630.0440.0910.1271.0000.1010.0630.230
CustomerType0.1470.1460.0460.1650.1310.1270.0070.0370.0680.0830.1300.1050.1410.0530.1530.0660.0180.1450.4060.1160.1550.1220.1010.1011.0000.0630.100
RequiredCarParkingSpaces0.0760.0110.0100.0500.0400.0000.0070.0170.0260.0400.0520.0400.2470.0150.0170.0290.0250.0380.0990.0760.0600.0590.0730.0630.0631.0000.174
ReservationStatus0.1720.0830.0260.0700.0250.0190.0680.0400.0530.0220.0850.0811.0000.0340.0870.0740.0150.0810.1830.0990.1040.0580.1310.2300.1000.1741.000

Missing values

2023-11-24T11:45:41.968175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-24T11:45:42.268203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IsCanceledLeadTimeArrivalDateYearArrivalDateMonthArrivalDateWeekNumberArrivalDateDayOfMonthStaysInWeekendNightsStaysInWeekNightsAdultsChildrenBabiesMealCountryMarketSegmentDistributionChannelIsRepeatedGuestPreviousCancellationsPreviousBookingsNotCanceledReservedRoomTypeAssignedRoomTypeBookingChangesDepositTypeAgentCompanyDaysInWaitingListCustomerTypeADRRequiredCarParkingSpacesTotalOfSpecialRequestsReservationStatusReservationStatusDate
003422015July27100200BBPRTDirectDirect000CC3No DepositNULLNULL0Transient0.000Check-Out2015-07-01
107372015July27100200BBPRTDirectDirect000CC4No DepositNULLNULL0Transient0.000Check-Out2015-07-01
2072015July27101100BBGBRDirectDirect000AC0No DepositNULLNULL0Transient75.000Check-Out2015-07-02
30132015July27101100BBGBRCorporateCorporate000AA0No Deposit304NULL0Transient75.000Check-Out2015-07-02
40142015July27102200BBGBROnline TATA/TO000AA0No Deposit240NULL0Transient98.001Check-Out2015-07-03
50142015July27102200BBGBROnline TATA/TO000AA0No Deposit240NULL0Transient98.001Check-Out2015-07-03
6002015July27102200BBPRTDirectDirect000CC0No DepositNULLNULL0Transient107.000Check-Out2015-07-03
7092015July27102200FBPRTDirectDirect000CC0No Deposit303NULL0Transient103.001Check-Out2015-07-03
81852015July27103200BBPRTOnline TATA/TO000AA0No Deposit240NULL0Transient82.001Canceled2015-05-06
91752015July27103200HBPRTOffline TA/TOTA/TO000DD0No Deposit15NULL0Transient105.500Canceled2015-04-22
IsCanceledLeadTimeArrivalDateYearArrivalDateMonthArrivalDateWeekNumberArrivalDateDayOfMonthStaysInWeekendNightsStaysInWeekNightsAdultsChildrenBabiesMealCountryMarketSegmentDistributionChannelIsRepeatedGuestPreviousCancellationsPreviousBookingsNotCanceledReservedRoomTypeAssignedRoomTypeBookingChangesDepositTypeAgentCompanyDaysInWaitingListCustomerTypeADRRequiredCarParkingSpacesTotalOfSpecialRequestsReservationStatusReservationStatusDate
4005001732017August352749200HBPRTOffline TA/TOTA/TO000DD1No Deposit181NULL0Transient-Party168.9201Check-Out2017-09-09
4005102642017August3426410200BBNLDOffline TA/TOTA/TO000DD2No Deposit71NULL0Transient89.7900Check-Out2017-09-09
4005202072017August3426410200HBGBROffline TA/TOTA/TO000EE0No Deposit143NULL0Transient131.7002Check-Out2017-09-09
4005302692017August3424413200BBGBROffline TA/TOTA/TO000DD0No Deposit40NULL0Contract84.8002Check-Out2017-09-10
4005401692017August353029200BBIRLDirectDirect000EE0No Deposit250NULL0Transient-Party204.2701Check-Out2017-09-10
4005502122017August353128210BBGBROffline TA/TOTA/TO000AA1No Deposit143NULL0Transient89.7500Check-Out2017-09-10
4005601692017August353029200BBIRLDirectDirect000EE0No Deposit250NULL0Transient-Party202.2701Check-Out2017-09-10
4005702042017August3529410200BBIRLDirectDirect000EE0No Deposit250NULL0Transient153.5703Check-Out2017-09-12
4005802112017August3531410200HBGBROffline TA/TOTA/TO000DD0No Deposit40NULL0Contract112.8001Check-Out2017-09-14
4005901612017August3531410200HBDEUOffline TA/TOTA/TO000AA0No Deposit69NULL0Transient99.0600Check-Out2017-09-14

Duplicate rows

Most frequently occurring

IsCanceledLeadTimeArrivalDateYearArrivalDateMonthArrivalDateWeekNumberArrivalDateDayOfMonthStaysInWeekendNightsStaysInWeekNightsAdultsChildrenBabiesMealCountryMarketSegmentDistributionChannelIsRepeatedGuestPreviousCancellationsPreviousBookingsNotCanceledReservedRoomTypeAssignedRoomTypeBookingChangesDepositTypeAgentCompanyDaysInWaitingListCustomerTypeADRRequiredCarParkingSpacesTotalOfSpecialRequestsReservationStatusReservationStatusDate# duplicates
18211922016February92612200UndefinedPRTGroupsTA/TO010AA0Non Refund134NULL0Transient79.0000Canceled2015-12-1055
222112592015September381703200BBGBRGroupsCorporate010AA0Non RefundNULL2230Transient40.0500Canceled2015-01-2150
185311062016March132413200FBPRTGroupsDirect000AA0Non Refund68NULL0Transient84.0000Canceled2016-01-2240
222012592015September381703100BBGBRGroupsCorporate010AA0Non RefundNULL2230Transient33.3000Canceled2015-01-2140
6040402017January31612100UndefinedPRTGroupsDirect000AA0No DepositNULLNULL0Transient-Party55.0000Check-Out2017-01-1929
17651742016February92612200UndefinedPRTGroupsTA/TO000AA0Non Refund68NULL0Transient70.0000Canceled2016-01-2129
195111402016February71202200HBPRTGroupsTA/TO000AA0Non RefundNULLNULL0Transient50.0000Canceled2015-12-1129
17751782015October41912200FBESPGroupsTA/TO000AA0Non Refund134NULL0Transient92.0000Canceled2015-08-1428
225212772016June251222200UndefinedPRTGroupsTA/TO000AA0Non RefundNULLNULL0Transient110.0000Canceled2016-03-2928
231713932016October41822200HBPRTGroupsTA/TO000AA0Non Refund68NULL0Transient72.0000Canceled2015-12-1728